class_docstr: EXPERIMENTAL
  
  AutoGluon InterpretableTabularPredictor predicts values in a column of a tabular dataset (classification or regression).
  InterpretableTabularPredictor shares the same functionality as TabularPredictor, but is limited to simple models
  that are easier to interpret visually via simple rules.
  
  InterpretableTabularPredictor should be used when accuracy is not important,
  and instead interpretability is the key requirement.
  
  Categorical features are one-hot-encoded to preserve interpretability.
  
  Stacking and bagging are not available in this predictor to preserve interpretability.
docstr: Print the rules of the highest performing model below the complexity threshold.
  
  Parameters
  ----------
  complexity_threshold : int, default=10
      Threshold for complexity (number of rules) of fitted models to show.
      If not model complexity is below this threshold, prints the model with the lowest complexity.
  model_name : str,  default=None
      Optionally print rules for a particular model, ignoring the complexity threshold.
docstr_tok: ["Print","the","rules","of","the","highest","performing","model","below","the","complexity","threshold",".","Parameters","--","--","--","--","--","complexity_threshold",":","int",",","default=10","Threshold","for","complexity","(","number","of","rules",")","of","fitted","models","to","show",".","If","not","model","complexity","is","below","this","threshold",",","prints","the","model","with","the","lowest","complexity",".","model_name",":","str",",","default=None","Optionally","print","rules","for","a","particular","model",",","ignoring","the","complexity","threshold","."]
code: def print_interpretable_rules(
      self, complexity_threshold: int = 10, model_name: str = None
  ):
      """
      Print the rules of the highest performing model below the complexity threshold.
  
      Parameters
      ----------
      complexity_threshold : int, default=10
          Threshold for complexity (number of rules) of fitted models to show.
          If not model complexity is below this threshold, prints the model with the lowest complexity.
      model_name : str,  default=None
          Optionally print rules for a particular model, ignoring the complexity threshold.
      """
      if model_name is None:
          summaries = self.leaderboard_interpretable()
          summaries_filtered = summaries[
              summaries.complexity <= complexity_threshold
          ]
          if summaries_filtered.shape[0] == 0:
              summaries_filtered = summaries
          model_name = summaries_filtered.iloc[0][
              "model"
          ]  # best model is at top
      agmodel = self._trainer.load_model(model_name)
      imodel = agmodel.model
      print(imodel)
  
file_path: autogluon/tabular/src/autogluon/tabular/predictor/interpretable_predictor.py
imports: import logging
  import numpy
  import pandas
  from .predictor import TabularPredictor
label_desc: Use image node_id 4 for calling the InterpretableTabularPredictor obj's underlying member method code with example usage: obj.print_interpretable_rules(complexity_threshold, model_name) without return types